Bibliographic record
Abstract
As social media use continues to rise, studies have linked high social media use with rising levels of depression, particularly in young adults. This narrative has pervaded, yet in the research thus far, there is no general consensus as to causation or direction. What remains constant is that when mediators such as 'comparison' and 'envy' are introduced between social media use and depression, there is a negative correlation. In a qualitative study, I examine the connection between social comparison, Instagram use, and envy in young women. I conducted semi-structured interviews with a group of 10 female university students between the ages of 18-24. Interviews were analysed through qualitative descriptive analysis. Overwhelmingly, subjects engaged in frequent social comparison offline, which translated to frequent social comparison, made worse, on Instagram. As a result, participants admitted to feeling envious as well as other feelings like frustration, loneliness, anger, and overwhelm. However, users also reported positive experiences such as inspiration, humour, motivation, and happiness, when they are on Instagram. Offline affect proved to be the biggest moderators and indicators of comparison and the positive or negative experiences of the participants. This research may suggest future care in this area should focus on offline affect rather than the social networks themselves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".